AI and Climate Change
The Art
The Science
“…Electricity demand for data centres more than doubles by 2030
Data centre electricity consumption is set to more than double to around 945 TWh by 2030. This is slightly more than Japan’s total electricity consumption today. AI is the most important driver of this growth, alongside growing demand for other digital services. The United States accounts for by far the largest share of this projected increase, followed by China. In the United States, data centres account for nearly half of electricity demand growth between now and 2030. By the end of the decade, the country is set to consume more electricity for data centres than for the production of aluminium, steel, cement, chemicals and all other energy-intensive goods combined. ….”
“…There are large uncertainties in the outlook for AI-related electricity demand
There are uncertainties in how quickly AI will be adopted, how capable and productive it will become, how fast efficiency improvements will occur, and whether bottlenecks in the energy sector can be resolved. These uncertainties are explored in sensitivity cases. A Lift- Off Case assumes higher rates of AI uptake and proactive action to reduce energy sector bottlenecks. A Headwinds Case incorporates bottlenecks – including macroeconomic headwinds – in the uptake of AI and the buildout of energy infrastructure to power it. Our High Efficiency Case highlights the potential for even stronger gains in the efficiency of AI- related hardware and AI models. In this case, electricity demand from data centres is 20% lower in 2035 than in the Base Case. By 2035, the range of data centre electricity demand across our cases spans from 700 to 1 700 TWh. The increase in gas-fired power to meet data centre demand in our Lift-Off Case is four times higher than in our Headwinds Case. Growth in nuclear output to meet data centre demand varies even more…”
AI and climate change
The emergence of AI has both raised concerns that AI-fuelled data centre growth might fuel climate change and also raised expectations that AI applications in the energy sector could help reduce emissions by unlocking new optimisations and efficiencies. As over 100 countries – and the European Union – have targets to reach net zero emissions between 2030 and 2070, it is pertinent to explore what AI’s impact on emissions could potentially be. …
…AI applications in the energy sector are being used for a wide range of optimisations, some of which lead to emissions reductions, whether directly through reduced energy needs or otherwise:
Methane emissions reductions in oil and gas operations – a large source of this sector’s methane emissions come from leaks; AI can facilitate detection so that repairs can happen sooner, for example through better identification using satellite monitoring systems.
Power sector emissions reductions by improving efficiencies at fossil fuel-powered plants; for example, by ensuring process conditions within a natural gas-powered plant are closer to those for optimal efficiency.
Industry emissions reductions by optimising manufacturing processes for their energy needs, therefore lowering related emissions; for example, improving the fuel mix for cement production can improve energy efficiency by more than 2%.
Transport emissions reductions through more efficient vehicle operations and utilisation; for example, improved route choice or driving characteristics lead to efficiency gains of 5-10% and hence reduce emissions.
Buildings emissions reductions by optimising energy consumption in buildings equipped with management systems; for example, an optimised heating, ventilation and air conditioning control can save around 10% in energy consumption.
The adoption of existing AI applications in end-use sectors could lead to 1 400 Mt of CO2 emissions reductions in 2035 in the Widespread Adoption Case. This does not include any breakthrough discoveries that may emerge thanks to AI in the next decade. These potential emissions reductions, if realised, would be three times larger than the total data centre emissions in the Lift-off Case, and four times larger than those in the Base Case. ..”
‘…It is vital to note that there is currently no momentum that could ensure the widespread adoption of these AI applications. Therefore, their aggregate impact, even in 2035, could be marginal if the necessary enabling conditions are not created. Barriers include constraints on access to data, the absence of digital infrastructure and skills, regulatory and security restrictions, and social or cultural obstacles. They could be negated by rebound effects, such as those enabled by modal shifts away from public transport towards autonomous cars. The net impact of AI on emissions – and therefore climate change – will depend on how AI applications are rolled out, what incentives and business cases arise, and how regulatory frameworks respond to the evolving AI landscape…”
IEA (2025), Energy and AI, IEA, Paris https://www.iea.org/reports/energy-and-ai, Licence: CC BY 4.0, Executive Summary
